# coding=utf-8
# Adapted from
# https://huggingface.co/core42/jais-30b-chat-v3/blob/main/modeling_jais.py
# Copyright 2023 The vLLM team.
# Copyright 2023 the Jais authors and HuggingFace Inc. team.  All rights
# reserved.
# Copyright 2023 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Jais model compatible with HuggingFace weights."""

import math
from typing import Iterable, List, Optional, Tuple

import torch
from torch import nn

from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig
from vllm.distributed import (get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size)
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import SamplerOutput
from vllm.transformers_utils.configs import JAISConfig


class SwiGLUActivation(nn.Module):

    def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
        return x1 * nn.functional.silu(x2)


def _get_alibi_slopes(n):

    def get_slopes_power_of_2(n):
        start = 2**(-(2**-(math.log2(n) - 3)))
        ratio = start
        return [start * ratio**i for i in range(n)]

    if math.log2(n).is_integer():
        return get_slopes_power_of_2(n)
    else:
        closest_power_of_2 = 2**math.floor(math.log2(n))
        return (get_slopes_power_of_2(closest_power_of_2) + _get_alibi_slopes(
            2 * closest_power_of_2)[0::2][:n - closest_power_of_2])


class JAISAttention(nn.Module):

    def __init__(
        self,
        config: JAISConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        total_num_heads = config.num_attention_heads
        tensor_model_parallel_world_size = (
            get_tensor_model_parallel_world_size())
        assert total_num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = total_num_heads // tensor_model_parallel_world_size
        self.head_dim = self.hidden_size // total_num_heads
        if hasattr(config, "scale_qk_dot_by_d"):
            config.mup_scale_qk_dot_by_d = config.scale_qk_dot_by_d
        self.attn_scale_power = 1.0 if config.mup_scale_qk_dot_by_d else 0.5
        self.scale = self.head_dim**-self.attn_scale_power

        self.c_attn = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            total_num_heads,
            bias=True,
            quant_config=quant_config,
        )
        self.c_proj = RowParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=True,
            quant_config=quant_config,
        )

        tp_rank = get_tensor_model_parallel_rank()
        head_start = tp_rank * self.num_heads
        head_end = (tp_rank + 1) * self.num_heads
        alibi_slopes = _get_alibi_slopes(total_num_heads)
        alibi_slopes = alibi_slopes[head_start:head_end]
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            scale=self.scale,
            alibi_slopes=alibi_slopes,
            cache_config=cache_config,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        qkv, _ = self.c_attn(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
        attn_output, _ = self.c_proj(attn_output)
        return attn_output


class JAISMLP(nn.Module):

    def __init__(
        self,
        intermediate_size: int,
        config: JAISConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        hidden_size = config.hidden_size
        self.swiglu = config.activation_function == "swiglu"
        self.c_fc = ColumnParallelLinear(
            hidden_size,
            intermediate_size,
            bias=True,
            quant_config=quant_config,
        )
        self.c_fc2 = (ColumnParallelLinear(
            hidden_size,
            intermediate_size,
            bias=True,
            quant_config=quant_config,
        ) if self.swiglu else None)
        self.c_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=True,
            quant_config=quant_config,
        )

        self.act = SwiGLUActivation()

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        if self.swiglu:
            hidden_states2, _ = self.c_fc2(hidden_states)
        hidden_states, _ = self.c_fc(hidden_states)
        hidden_states = (self.act(hidden_states, hidden_states2)
                         if self.swiglu else self.act(hidden_states))
        hidden_states, _ = self.c_proj(hidden_states)
        return hidden_states


class JAISBlock(nn.Module):

    def __init__(
        self,
        config: JAISConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        hidden_size = config.hidden_size
        inner_dim = (config.n_inner if config.n_inner is not None else 4 *
                     hidden_size)

        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.attn = JAISAttention(config, cache_config, quant_config)
        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.mlp = JAISMLP(inner_dim, config, quant_config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_output = self.attn(
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )
        # residual connection
        hidden_states = attn_output + residual

        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        # residual connection
        hidden_states = residual + feed_forward_hidden_states
        return hidden_states


class JAISModel(nn.Module):

    def __init__(
        self,
        config: JAISConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        assert not config.add_cross_attention
        assert not config.scale_attn_by_inverse_layer_idx
        assert not config.reorder_and_upcast_attn
        self.embed_dim = config.hidden_size
        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
        self.wpe = (nn.Embedding(config.max_position_embeddings,
                                 self.embed_dim)
                    if config.position_embedding_type != "alibi" else None)
        if hasattr(config, "embeddings_scale"):
            self.embeddings_scale = config.embeddings_scale
        else:
            self.embeddings_scale = config.mup_embeddings_scale
        self.h = nn.ModuleList([
            JAISBlock(config, cache_config, quant_config)
            for _ in range(config.num_hidden_layers)
        ])
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        inputs_embeds = self.wte(input_ids)
        if self.wpe is not None:
            position_embeds = self.wpe(position_ids)
            hidden_states = inputs_embeds + position_embeds
        else:
            hidden_states = inputs_embeds
        hidden_states *= torch.tensor(float(self.embeddings_scale),
                                      dtype=hidden_states.dtype)

        for i in range(len(self.h)):
            layer = self.h[i]
            hidden_states = layer(hidden_states, kv_caches[i], attn_metadata)

        hidden_states = self.ln_f(hidden_states)
        return hidden_states


class JAISLMHeadModel(nn.Module):

    def __init__(
        self,
        config: JAISConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        self.quant_config = quant_config
        self.transformer = JAISModel(config, cache_config, quant_config)
        self.lm_head_weight = self.transformer.wte.weight
        if hasattr(config, "width_scale"):
            self.output_logits_scale = config.width_scale
        else:
            self.output_logits_scale = (config.mup_output_alpha *
                                        config.mup_width_scale)
        self.logits_processor = LogitsProcessor(vocab_size=config.vocab_size,
                                                scale=self.output_logits_scale)
        self.sampler = Sampler()

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        hidden_states = self.transformer(input_ids, positions, kv_caches,
                                         attn_metadata)
        return hidden_states

    def compute_logits(self, hidden_states: torch.Tensor,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head_weight, hidden_states,
                                       sampling_metadata)
        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        for name, loaded_weight in weights:
            if "lm_head.weight" in name:
                # GPT-2 ties the weights of the embedding layer and the final
                # linear layer.
                continue
            if ".attn.bias" in name or ".attn.masked_bias" in name:
                # Skip attention mask.
                # NOTE: "c_attn.bias" should not be skipped.
                continue
            if "relative_pe" in name:
                continue
            if not name.startswith("transformer."):
                name = "transformer." + name
            param = params_dict[name]
            # The HF's GPT-2 implementation uses Conv1D instead of Linear.
            # Because of this, we need to transpose the weights.
            # Note(zhuohan): the logic below might break quantized models.
            for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
                if conv1d_weight_name not in name:
                    continue
                if not name.endswith(".weight"):
                    continue
                loaded_weight = loaded_weight.t()
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)
